Evolving Biological Technologies as Engineered Drinking Water Treatment Systems for Cyanotoxin Removal: Towards an Improved Predictive Understanding of the Microorganisms Involved

Harmful algae blooms associated with toxic cyanobacteria have been increasing in both frequency and severity as a result of global climate change. These blooms are responsible for the release of biotoxins, the most common and toxic of which are the microcystins (MCs), that are recalcitrant to the operations of conventional drinking water treatment plants (DWTPs). Bio-based technologies targeting MC removal, such as biofiltration systems, have been proposed as cost-effective and sustainable alternatives to advanced treatment technologies (i.e., ozonation). Biofilters rely on native bacterial communities endemic to the source water to metabolize microcystin as a carbon and energy source. However, biofilter treatment variability is an ongoing challenge, arising from variations in environmental conditions including temperature, pH, and the presence of other bioavailable nutrients. To effectively address this treatment variability, biofilters must be evolved into “engineered” systems, in which MC degrader bioactivity is promoted and treatment tightly controlled. This dissertation has amalgamated a series of system modelling, experimental data collection efforts, unstructured kinetic modelling, as well as sensitivity and optimal experimental design (OED) analyses to arrive at an improved predictive understanding of MC biodegradation. The results of these efforts first indicated that the removal of biotoxins within biofilters is highly dependent on the pretreatment system and operations employed by the DWTP. The kinetics of MC biodegradation by degrading communities were bi-phasic, where the taxonomy of the communities and the kinetics of MC degradation were altered in the presence of an alternative carbon source. The kinetics of MC metabolism and bacterial growth of isolated degrading populations were well predicted by the Moser kinetic model, where up to 5 out of 6 parameters could be identified. A novel approach to global sensitivity analysis was developed to improve the accuracy and convergence efficiency of the sensitivity indices ranking the most influential parameters of the Moser model. Finally, the OED procedure designed a fed batch reactor experiment that drastically improved the practical identifiability of the parameters of the Moser model. The culmination of these analyses has laid the foundation for a comprehensive and practical kinetic model describing degrader growth and MC removal in bio-based treatment systems